Natural Language Intent Recognition for PCB Design Automation
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Solution Overview
Problem
The process of electronic design connectivity capture and review in PCB design is manual, time-consuming, and requires significant learning cycles to understand tool-specific commands and operations, lacking seamless intent recognition.
Innovation Solution
A method and system for intelligent intent recognition using natural language inputs, which includes an intent recognition model that interprets user inputs, generates commands, and executes them in a target tool environment, incorporating training data and feedback loops for improved accuracy and ease of use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user inputs using keystrokes or mouse strokes are used for electronic design connectivity capture and review, then the process can be completed with existing tools, but the process becomes time-consuming and requires significant learning cycles
Solution Approach 1:
The patent replaces manual mechanical interactions (keystrokes, mouse strokes) with natural language processing and intent recognition technology. The system uses speech or text inputs that are processed by machine learning models to automatically generate and execute design commands, eliminating the need for users to manually navigate complex tool interfaces and learn numerous commands.
Solution Approach 2:
The system enables self-service by automatically understanding user intent and executing appropriate commands without requiring manual step-by-step guidance. The intent recognition model autonomously interprets natural language inputs, determines the desired design operations, and performs the connectivity capture and review tasks independently, reducing both time required and learning effort.
2Measurement precision
If manual step-by-step execution is required to achieve desired results, then precise control over design operations is maintained, but learning cycles increase and user effort multiplies
Solution Approach 1:
The patent introduces an intermediary layer consisting of the intent recognition model and command generator that sits between the user and the design tool. This intermediary automatically translates natural language intent into precise tool-specific commands, maintaining control precision while shielding users from the complexity of manual step-by-step execution and numerous tool commands.
Solution Approach 2:
The system segments the complex design process into distinct functional components: intent recognition, command generation, and command execution. Each component handles a specific aspect of the workflow, allowing the system to maintain precise control over operations while reducing the perceived complexity for users who only need to provide high-level natural language instructions.
3Adaptability or versatility
If tool-specific commands and operations are used, then functionality is fully utilized, but learning effort increases for new users
Solution Approach 1:
The patent creates a universal natural language interface that can perform multiple design functions without requiring users to learn tool-specific commands. The intent recognition model is designed to handle various design operations (connectivity capture, review, analysis) through a single unified interface that accepts natural language inputs, making the system adaptable to different tasks while maintaining ease of use.
Solution Approach 2:
The system uses machine learning models trained on tool-specific commands and operations to create a knowledge base that copies and understands the functionality of complex design tools. This trained model enables the system to leverage full tool functionality while presenting a simplified natural language interface, eliminating the need for users to learn numerous specialized commands.
Data Source
AI summary
Embodiments include herein are directed towards a system and method for intelligent intent recognition based electronic design. Embodiments may include receiving, using a processor, a natural language input from a user at an intent recognition model. Embodiments may also include performing intent recognition on the natural language input at the intent recognition model and providing an output from the intent recognition model to a command generator. Embodiments may further include generating a command based upon, at least in part, the output and executing the command at a target tool environment.